Association of polygenic scores with chronic kidney disease phenotypes in a longitudinal study of older adults
Authors: Andrew Bakshi, Julia Jefferis, Rory Wolfe, James B. Wetmore, John J. McNeil, Anne M. Murray, Kevan R. Polkinghorne, Andrew J. Mallett, Paul Lacaze
Journal: Kidney International (2023)

Risk of chronic kidney disease (CKD) is influenced by environmental and genetic factors and increases sharply in individuals 70 years and older. Polygenic scores (PGS) for kidney disease-related traits have shown promise but require validation in well-characterized cohorts. Here, we assessed the performance of recently developed PGSs for CKD-related traits in a longitudinal cohort of healthy older individuals enrolled in the Australian ASPREE randomized controlled trial of daily low-dose aspirin with CKD risk at baseline and longitudinally. Among 11,813 genotyped participants aged 70 years or more with baseline eGFR measures, we tested associations between PGSs and measured eGFR at baseline, clinical phenotype of CKD, and longitudinal rate of eGFR decline spanning up to six years of follow-up per participant. A PGS for eGFR was associated with baseline eGFR, with a significant decrease of 3.9 mL/min/1.73m2 (95% confidence interval -4.17 to -3.68) per standard deviation (SD) increase of the PGS. This PGS, as well as a PGS for CKD stage 3 were both associated with higher risk of baseline CKD stage 3 in cross-sectional analysis (Odds Ratio 1.75 per SD, 95% confidence interval 1.66-1.85, and Odds Ratio 1.51 per SD, 95% confidence interval 1.43-1.59, respectively). Longitudinally, two separate PGSs for eGFR slope were associated with significant kidney function decline during follow-up. Thus, our study demonstrates that kidney function has a considerable genetic component in older adults, and that new PGSs for kidney disease-related phenotypes may have potential utility for CKD risk prediction in advanced age. Risk of chronic kidney disease (CKD) is influenced by environmental and genetic factors and increases sharply in individuals 70 years and older. Polygenic scores (PGS) for kidney disease-related traits have shown promise but require validation in well-characterized cohorts. Here, we assessed the performance of recently developed PGSs for CKD-related traits in a longitudinal cohort of healthy older individuals enrolled in the Australian ASPREE randomized controlled trial of daily low-dose aspirin with CKD risk at baseline and longitudinally. Among 11,813 genotyped participants aged 70 years or more with baseline eGFR measures, we tested associations between PGSs and measured eGFR at baseline, clinical phenotype of CKD, and longitudinal rate of eGFR decline spanning up to six years of follow-up per participant. A PGS for eGFR was associated with baseline eGFR, with a significant decrease of 3.9 mL/min/1.73m2 (95% confidence interval -4.17 to -3.68) per standard deviation (SD) increase of the PGS. This PGS, as well as a PGS for CKD stage 3 were both associated with higher risk of baseline CKD stage 3 in cross-sectional analysis (Odds Ratio 1.75 per SD, 95% confidence interval 1.66-1.85, and Odds Ratio 1.51 per SD, 95% confidence interval 1.43-1.59, respectively). Longitudinally, two separate PGSs for eGFR slope were associated with significant kidney function decline during follow-up. Thus, our study demonstrates that kidney function has a considerable genetic component in older adults, and that new PGSs for kidney disease-related phenotypes may have potential utility for CKD risk prediction in advanced age. Lay SummaryThe sum of multiple small genetics contributors to a trait such as kidney function can be captured by polygenic risk scores (PGSs), derived from large-scale genome-wide association studies; however, the clinical significance of these scores is still being understood. We tested several recently developed PGSs for kidney traits in 11,813 European Australians, aged >70 years, from the ASPirin in Reducing Events in the Elderly population, with a mean estimated glomerular filtration rate (eGFR) of 72.9 ml/min per 1.73 m2. We validated multiple PGSs for both eGFR and decline in eGFR. At a population level, those with higher PGSs had the lowest mean eGFR of 57 ml/min per 1.73 m2, with significant individual variability. Overall, this study validated PGSs for kidney function in an older population, showing PGSs, which are static across the life span, still impact kidney function in later stages of life. The sum of multiple small genetics contributors to a trait such as kidney function can be captured by polygenic risk scores (PGSs), derived from large-scale genome-wide association studies; however, the clinical significance of these scores is still being understood. We tested several recently developed PGSs for kidney traits in 11,813 European Australians, aged >70 years, from the ASPirin in Reducing Events in the Elderly population, with a mean estimated glomerular filtration rate (eGFR) of 72.9 ml/min per 1.73 m2. We validated multiple PGSs for both eGFR and decline in eGFR. At a population level, those with higher PGSs had the lowest mean eGFR of 57 ml/min per 1.73 m2, with significant individual variability. Overall, this study validated PGSs for kidney function in an older population, showing PGSs, which are static across the life span, still impact kidney function in later stages of life. Chronic kidney disease (CKD) is a significant and growing health care burden worldwide.1Kalantar-Zadeh K. Jafar T.H. Nitsch D. et al.Chronic kidney disease.Lancet. 2021; 398: 786-802Abstract Full Text Full Text PDF PubMed Scopus (320) Google Scholar CKD prevalence increases with age, with peak incidence after the age of 60 years.2Xie Y. Bowe B. Mokdad A.H. et al.Analysis of the Global Burden of Disease study highlights the global, regional, and national trends of chronic kidney disease epidemiology from 1990 to 2016.Kidney Int. 2018; 94: 567-581Abstract Full Text Full Text PDF PubMed Scopus (467) Google Scholar Risk is driven by conventional risk factors, including hypertension, diabetes, obesity, and smoking.2Xie Y. Bowe B. Mokdad A.H. et al.Analysis of the Global Burden of Disease study highlights the global, regional, and national trends of chronic kidney disease epidemiology from 1990 to 2016.Kidney Int. 2018; 94: 567-581Abstract Full Text Full Text PDF PubMed Scopus (467) Google Scholar,3Levey A.S. Coresh J. Chronic kidney disease.Lancet. 2012; 379: 165-180Abstract Full Text Full Text PDF PubMed Scopus (1388) Google Scholar In addition, CKD is strongly influenced by genetic factors, especially in monogenic kidney disorders.4Aung T.T. Bhandari S.K. Chen Q. et al.Autosomal dominant polycystic kidney disease prevalence among a racially diverse United States population, 2002 through 2018.Kidney360. 2021; 2: 2010-2015Crossref PubMed Scopus (6) Google Scholar,5Mallett A. Patel C. Salisbury A. et al.The prevalence and epidemiology of genetic renal disease amongst adults with chronic kidney disease in Australia.Orphanet J Rare Dis. 2014; 9: 98Crossref PubMed Scopus (47) Google Scholar Although rare monogenic variants are associated with severe, rare forms of CKD (e.g., autosomal dominant polycystic kidney disease and Alport syndrome), the relationship between common genetic variants and CKD risk is less well characterized, particularly in older individuals in whom CKD prevalence is high.6Ravani P. Quinn R. Fiocco M. et al.Association of age with risk of kidney failure in adults with stage IV chronic kidney disease in Canada.JAMA Netw Open. 2020; 3e2017150Crossref Scopus (18) Google Scholar,7Tonelli M. Riella M. World Kidney Day Steering Committee2014. Chronic kidney disease and the aging population: World Kidney Day 2014.Transplantation. 2014; 97: 490-493Crossref PubMed Scopus (6) Google Scholar Heritability estimates for kidney function generated from twin studies suggest a diminishing but still significant genetic component across the life span.8Jefferis J. Pelecanos A. Catts V. Mallett A. The heritability of kidney function using an older Australian twin population.Kidney Int Rep. 2022; 7: 1819-1830Abstract Full Text Full Text PDF PubMed Scopus (2) Google Scholar Genome-wide association studies predict lower heritability of CKD between 7% and 18%, depending on the population, but have been generated from younger populations.9Gorski M. Jung B. Li Y. et al.Meta-analysis uncovers genome-wide significant variants for rapid kidney function decline.Kidney Int. 2021; 99: 926-939Abstract Full Text Full Text PDF PubMed Scopus (26) Google Scholar, 10Sinnott-Armstrong N. Tanigawa Y. Amar D. et al.Genetics of 35 blood and urine biomarkers in the UK Biobank.Nat Genet. 2021; 53: 185-194Crossref PubMed Scopus (201) Google Scholar, 11Hellwege J.N. Velez Edwards D.R. Giri A. et al.Mapping eGFR loci to the renal transcriptome and phenome in the VA Million Veteran Program.Nat Commun. 2019; 10: 3842Crossref PubMed Scopus (70) Google Scholar, 12Wuttke M. Li Y. Li M. et al.A catalog of genetic loci associated with kidney function from analyses of a million individuals.Nat Genet. 2019; 51: 957-972Crossref PubMed Scopus (345) Google Scholar Further studies are required to understand the relevance of genetic contributions to CKD in older age.8 Accurate risk prediction remains a clinical challenge for CKD, especially in older people. Early identification of high-risk individuals for CKD through genetic profiling, before decline in kidney function, may help target interventions to high-risk individuals earlier and reduce disease burden.6Ravani P. Quinn R. Fiocco M. et al.Association of age with risk of kidney failure in adults with stage IV chronic kidney disease in Canada.JAMA Netw Open. 2020; 3e2017150Crossref Scopus (18) Google Scholar,7Tonelli M. Riella M. World Kidney Day Steering Committee2014. Chronic kidney disease and the aging population: World Kidney Day 2014.Transplantation. 2014; 97: 490-493Crossref PubMed Scopus (6) Google Scholar Polygenic scores (PGSs) are an aggregate measure of common-variant risk for a given disease or trait and are calculated by summing the effects of many different single-nucleotide polymorphisms together into a single score. PGSs for traits associated with kidney function have been recently developed, including from the CKDGen Consortium and UK Biobank, based on phenotypes related to kidney failure, estimated glomerular filtration rate (eGFR), rate of decline in eGFR, urine albumin-creatinine ratio (UACR), and other conditions, such as diabetes mellitus and coronary artery disease.9Gorski M. Jung B. Li Y. et al.Meta-analysis uncovers genome-wide significant variants for rapid kidney function decline.Kidney Int. 2021; 99: 926-939Abstract Full Text Full Text PDF PubMed Scopus (26) Google Scholar,10Sinnott-Armstrong N. Tanigawa Y. Amar D. et al.Genetics of 35 blood and urine biomarkers in the UK Biobank.Nat Genet. 2021; 53: 185-194Crossref PubMed Scopus (201) Google Scholar,13Khera A.V. Chaffin M. Aragam K.G. et al.Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations.Nat Genet. 2018; 50: 1219-1224Crossref PubMed Scopus (1492) Google Scholar Older populations have more medical comorbidities, and PGSs for kidney function have been related to cardiac and metabolic disease (including diabetes mellitus) that are known to influence kidney function.14Kember RL, Verma A, Verma S, et al. Polygenic risk scores for cardio-renal-metabolic diseases in the Penn Medicine Biobank. bioRxiv. Published online September 9, 2019. https://doi.org/10.1101/759381Google Scholar,15Steinbrenner I. Yu Z. Jin J. et al.MO517: a polygenic risk score for reduced EGFR is associated with adverse events in a chronic kidney disease cohort—the German Chronic Kidney Disease.Nephrol Dial Transplant. 2022; 37gfac071.048.6Google Scholar Validation and comparison of these scores in older people, aged >70 years, has been lacking despite the high burden of kidney disease in this older age group. This study aims to assess the performance of recently developed kidney disease PGSs, with regard to association with the following: (i) CKD risk at baseline and (ii) more rapid decline in kidney function in older individuals, independent of environmental considerations and established risk factors. For the first question, we conducted a cross-sectional analysis using baseline measurements to examine whether a PGS for eGFR and, separately, UACR was associated with these measures in our cohort, and to compare the performance of a PGS for eGFR with PGSs from several studies specific to CKD phenotypes. For the second question, we examined 2 PGSs specific for the "decline in eGFR" phenotype to determine whether they were associated with a greater rate of eGFR decline prospectively using our longitudinal measures of eGFR. In addition, we investigated whether the PGS for eGFR was associated with metabolic disorders that strongly associate with CKD (baseline diabetes mellitus and incident cardiovascular disease and myocardial infarction), to better understand these linked risks.16House A.A. Wanner C. Sarnak M.J. et al.Heart failure in chronic kidney disease: conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Controversies Conference.Kidney Int. 2019; 95: 1304-1317Abstract Full Text Full Text PDF PubMed Scopus (187) Google Scholar The study includes genotyped Australian participants from the ASPirin in Reducing Events in the Elderly (ASPREE) trial, a randomized placebo-controlled trial of daily low-dose aspirin in older adults.17McNeil J.J. Nelson M.R. Woods R.L. et al.Effect of aspirin on all-cause mortality in the healthy elderly.N Engl J Med. 2018; 379: 1519-1528Crossref PubMed Scopus (511) Google Scholar, 18McNeil J.J. Wolfe R. Woods R.L. et al.Effect of aspirin on cardiovascular events and bleeding in the healthy elderly.N Engl J Med. 2018; 379: 1509-1518Crossref PubMed Scopus (660) Google Scholar, 19McNeil J.J. Woods R.L. Nelson M.R. et al.Effect of aspirin on disability-free survival in the healthy elderly.N Engl J Med. 2018; 379: 1499-1508Crossref PubMed Scopus (341) Google Scholar The analysis also includes recent data from the ASPREE-XT02 Longitudinal Data Set, which extends observational follow-up time beyond the original randomization period to a mean of 6.3 years. The ASPREE study design,20ASPREE Investigator GroupStudy design of ASPirin in Reducing Events in the Elderly (ASPREE): a randomized, controlled trial.Contemp Clin Trials. 2013; 36: 555-564Crossref PubMed Scopus (191) Google Scholar,21Nelson M.R. Reid C.M. Ames D.A. et al.Feasibility of conducting a primary prevention trial of low-dose aspirin for major adverse cardiovascular events in older people in Australia: results from the ASPirin in Reducing Events in the Elderly (ASPREE) pilot study.Med J Aust. 2008; 189: 105-109Crossref PubMed Scopus (63) Google Scholar recruitment, and baseline characteristics22Lockery J.E. Collyer T.A. Abhayaratna W.P. et al.Recruiting general practice patients for large clinical trials: lessons from the Aspirin in Reducing Events in the Elderly (ASPREE) study.Med J Aust. 2019; 210: 168-173Crossref PubMed Scopus (23) Google Scholar have been published previously. ASPREE participants were recruited in partnership with a network of general practitioners in Australia and the United States. The study was approved by the Alfred Hospital Human Research Ethics Committee (project 390/15) in accordance with the National Statement on Ethical Conduct in Human Research (2007). Written informed consent for genetic analysis was obtained from all study participants. For the present genetic analysis, we include only Australian ASPREE participants with a baseline eGFR measurement who provided a sample for DNA analysis (Supplementary Figure S1). First, we conducted cross-sectional analysis of the baseline cohort. Second, we followed up participants with longitudinal data for analysis of eGFR decline. Finally, we excluded any participants with stage 5 CKD at baseline (those on dialysis treatment or those who received a kidney transplant). There were 4 outcome measures for the cross-sectional analysis: (i) association with eGFR as a continuous variable; and (ii) the presence of CKD using established criteria, with CKD being defined according to 3 measures: (a) CKDKDIGO was based on Kidney Disease: Improving Global Outcomes (KDIGO) criteria, where CKD was defined as either eGFR <60 ml/min per 1.73 m2 or UACR >3.0 mg/mmol. CKDKDIGO was derived for the subset of participants who had UACR available at baseline. (b) CKDeGFR was defined as CKD using only eGFR <60 ml/min per 1.73 m2); (c) a moderate to severe CKD phenotype (CKDeGFR_mod) defined as eGFR <45 ml/min per 1.73 m2. The eGFR was calculated using the Chronic Kidney Disease Epidemiology Collaboration equation23Levey A.S. Stevens L.A. Schmid C.H. et al.A new equation to estimate glomerular filtration rate.Ann Intern Med. 2009; 150: 604-612Crossref PubMed Scopus (17329) Google Scholar and serum creatinine measured at baseline and annual study visits. UACR was measured at baseline (mg/mmol), with albuminuria defined as a UACR ≥3.0 mg/mmol. For the longitudinal analysis, we used the annual eGFR measures in a statistical model to draw inferences about annual decline in ml/min per 1.73 m2. Diabetes at baseline was based on presence of either self-report or fasting glucose ≥126 mg/dl, or medication for diabetes. Incident cardiovascular disease and myocardial infarction were classified as adjudicated outcomes in the original trial outcomes (adjudication assessed by expert committees, as described previously18McNeil J.J. Wolfe R. Woods R.L. et al.Effect of aspirin on cardiovascular events and bleeding in the healthy elderly.N Engl J Med. 2018; 379: 1509-1518Crossref PubMed Scopus (660) Google Scholar). Genotyping was performed on the Axiom 2.0 Precision Medicine Diversity Research Array (Thermo Fisher Scientific) following standard protocols and quality control. Variants were aligned to the human genome reference GRCh38; quality control steps were applied to match for predicted and expected sex, filter for relatedness (using PLINK 2.0 based on --king-table-filter 0.1) as well as missing samples and variants (--mind and --geno defaults), and exclude variants with Hardy-Weinberg P < 0.000001. Variants were imputed by TopMED Imputation Server Imputation (European samples).24Taliun D. Harris D.N. Kessler M.D. et al.Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program.Nature. 2021; 590: 290-299Crossref PubMed Scopus (553) Google Scholar Variants that were multiallelic or with low imputation quality scores (r2 < 0.3) were removed. To mitigate the effect of population stratification bias on the PGS (i.e., confounding related to ethnicity), we included only individuals of European genetic ancestry, identified by principal component analysis and overlapping the European subset of the 1000 Genomes Project phase 3 reference population25Consortium T 1000 GP, The 1000 Genomes Project Consortium. A global reference for human genetic variation.Nature. 2015; 526: 68-74Crossref PubMed Scopus (9289) Google Scholar (for details, see Supplementary Figure S2A and B). Briefly, the genotype data were combined with 1000 Genomes Project phase 3 data, with linkage disequilibrium (LD) pruned (r2 < 0.1). The Z score of the first 2 principal component eigenvectors was calculated, and used to exclude samples with ±2 SDs in Z score compared with 1000 Genomes Project phase 3 reference populations, with the European population used to identify individuals used in this study (Supplementary Figure S2A and B). The principal PGS for the cross sectional eGFR analysis was published by Yu et al, 2021 (PGS000883), hereby referred to as PGS_GFR.26Yu Z. Jin J. Tin A. et al.Polygenic risk scores for kidney function and their associations with circulating proteome, and incident kidney diseases.J Am Soc Nephrol. 2021; 32: 3161-3173Crossref PubMed Scopus (21) Google Scholar This score was then compared with the recently developed PGS for CKD stage 3, referred to as PGS_CKD_S3. For the assessment of eGFR decline, we examined 2 PGSs for rapid decline of glomerular filtration rate: PGS000664, hereby referred to as PGS_GFR_decline_2021, and PGS_GFR_decline_2022.9Gorski M. Jung B. Li Y. et al.Meta-analysis uncovers genome-wide significant variants for rapid kidney function decline.Kidney Int. 2021; 99: 926-939Abstract Full Text Full Text PDF PubMed Scopus (26) Google Scholar,27Gorski M. Rasheed H. Teumer A. et al.Genetic loci and prioritization of genes for kidney function decline derived from a meta-analysis of 62 longitudinal genome-wide association studies.Kidney Int. 2022; 102: 624-639Abstract Full Text Full Text PDF PubMed Scopus (9) Google Scholar We also examined a PGS for UACR (PGS000822, defined as PGS_ACR).28Teumer A. Li Y. Ghasemi S. et al.Genome-wide association meta-analyses and fine-mapping elucidate pathways influencing albuminuria.Nat Commun. 2019; 10: 4130Crossref PubMed Scopus (93) Google Scholar Table 1 briefly summarizes the PGSs examined, and lists details found in the primary publications.27Gorski M. Rasheed H. Teumer A. et al.Genetic loci and prioritization of genes for kidney function decline derived from a meta-analysis of 62 longitudinal genome-wide association studies.Kidney Int. 2022; 102: 624-639Abstract Full Text Full Text PDF PubMed Scopus (9) Google ScholarTable 1Comparison of available PGSs for eGFR and decline in eGFR: multiple PGSs for CKD have been generated using alternative strategies, including population, definition of eGFRPGSTraitPopulationDiscovery sample sizeDiscovery ancestryValidation cohortSNP-based heritabilityVariance explained, %Effect size in validation, HR or OR per SD (95% CI)LimitationsPGS000883 (PGS_GFR)Incident CKD = eGFR of 60 ml/min per 1.73m2Xie Y. Bowe B. Mokdad A.H. et al.Analysis of the Global Burden of Disease study highlights the global, regional, and national trends of chronic kidney disease epidemiology from 1990 to 2016.Kidney Int. 2018; 94: 567-581Abstract Full Text Full Text PDF PubMed Scopus (467) Google Scholar plus ≥30% eGFR decline during a follow-up visit compared with baselineUK Biobank+CKDGen1,159,871White = 82%;East Asian = 14.4%;South Asian = 1.8%;Black = 1.5%;Hispanic = 0.42%ARICNA7.3HR =1.21 (1.16–1.26)Generated in predominantly White population (83%) and validated in White populationPGP000269 (PGS_CKD_S3)Case = eGFR <60 ml/min per 1.73 m2Control = eGFR >90 ml/min per 1.73 m270% of UKBB Europeans177,208European/White = 100%CKDGen, UK Biobank, eMERGE-III, BioMe, UABNA4White = OR 1.46 (1.43–1.48)African = OR 1.23 (1.26–1.38)Latin = OR 1.42 (1.26–1.38)Asian = OR 1.68 (1.45 – 2.06)Dichotomizes eGFR, to account for variability in eGFR by ancestryStrength is well-validated multiethnic cohort, and addition of APOL1 in African cohortPGS000664 (PGS_decline_2021)eGFRcrea decline: 3 ml/min per 1.73 m2 per year, eGFRcrea decline ≥25%, and eGFRcrea <60 ml/min per 1.73 m2 at follow-up among those with eGFRcrea 60 ml/min per 1.73 m241 Studies from CKDGen and UK Biobank>2,700,000NAValidated alternate kidney markersNANANA"Extreme" phenotype of eGFR decline, unable to validate in non-White populationGorski 202227Gorski M. Rasheed H. Teumer A. et al.Genetic loci and prioritization of genes for kidney function decline derived from a meta-analysis of 62 longitudinal genome-wide association studies.Kidney Int. 2022; 102: 624-639Abstract Full Text Full Text PDF PubMed Scopus (9) Google Scholar (PGS_decline_2022)Decline in eGFR ml/min per year (eGFR at follow-up – eGFR at baseline/number of follow-up years)CKDGen, UK Biobank343,339White = 74%The Trøndelag Health StudyUnadjusted eGFR decline = 1%0.62NADistinction between cross-sectional CKD and decline in eGFR limited by collider biasPGS000822 (PGS_ACR)Cases = urine ACR > 30 mg/g, Control = urine ACR < 10 mg/gCKDGen, UK Biobank564,257White = 97%East Asian = 1.1%South Asian = 0.4%Black = 1.2%Latin = 0.3%CKDGen Consortium4.3%0.69Predominantly European population, variability in urine albumin/creatinine ratio, other studies have found variation in GWAS based on ancestory/disease statesARIC, Atherosclerosis Risk In Communities study; CI, confidence interval; CKD, chronic kidney disease; eGFR, estimated glomerular filtration rate; eMERGE-III, Electronic Medical Records and Genomics Phase III; GWAS, genome-wide association study; HR, hazard ratio; NA, not applicable; OR, odds ratio; PGS, polygenic score; SNP, single-nucleotide polymorphism; UAB, University of Alabama at Birmingham; UKBB, United Kingdom Biobank.Variances and heritability data are unavailable for some studies. Open table in a new tab ARIC, Atherosclerosis Risk In Communities study; CI, confidence interval; CKD, chronic kidney disease; eGFR, estimated glomerular filtration rate; eMERGE-III, Electronic Medical Records and Genomics Phase III; GWAS, genome-wide association study; HR, hazard ratio; NA, not applicable; OR, odds ratio; PGS, polygenic score; SNP, single-nucleotide polymorphism; UAB, University of Alabama at Birmingham; UKBB, United Kingdom Biobank. Variances and heritability data are unavailable for some studies. PGSs were calculated by PLINK2.0 using the score function.29Purcell S. Neale B. Todd-Brown K. et al.PLINK: a tool set for whole-genome association and population-based linkage analyses.Am J Hum Genet. 2007; 81: 559-575Abstract Full Text Full Text PDF PubMed Scopus (20990) Google Scholar,30Chang C.C. Chow C.C. Tellier L.C. et al.Second-generation PLINK: rising to the challenge of larger and richer datasets.Gigascience. 2015; 4: 7Crossref PubMed Scopus (5161) Google Scholar This function sums the effect sizes for each effect allele present in each participant, creating a single score for each participant. We created 3 predicted eGFR risk groups for PGS_GFR: high for those with PGS > 95% above the mean, medium for those with PGS between 5% and 95%, and low for those with PGS < 5%. The high PGS group is predicted to have the lowest eGFR, and hence would be expected to have the highest risk of CKD (Supplementary Figure S3). For the cross-sectional analysis, we used a linear regression to examine the relationship between PGS_GFR and eGFR at enrollment, and included the following covariates: age, sex, body mass index, alcohol use, smoking, hypertension, diabetes, and use of nonsteroidal anti-inflammatory drug or angiotensin-converting enzyme inhibitors and angiotensin receptor blockers. For the binary CKD outcomes, logistic regression was used to examine the relationship between CKD and PGS_GFR, using the same covariates as the linear regression model. For the longitudinal analysis, a linear mixed model examined the relationship between the PGS_GFR_decline_2021 and PGS_GFR_decline_2022 and repeated eGFR measurements over time in individual participants, with correlated random effects for participant-specific intercept and participant-specific slope using visit year as the time variable to yield annualized eGFR change (Supplementary Table S1). Covariates included in the model were baseline age, sex, body mass index, alcohol and smoking, hypertension, diabetes, and treatment arm (aspirin or placebo). To quantify the relationship between rate of decline of eGFR and PGS_GFR_decline_2021/2022, the linear mixed model included an interaction term between the PGS and visit year. We used the glht function from R package multcomp31Hothorn T, Bretz F, Westfall P. Package multcomp: simultaneous inference in general parametric models. CRAN repository. Accessed May 2, 2022. https://scholars.ttu.edu/en/publications/package-multcomp-simultaneous-inference-in-general-parametric-modGoogle Scholar to derive point estimates of the rate of change per year for individuals estimated to lie at: (i) 2 SDs above mean decline, (ii) 2 SDs below mean decline, and (iii) mean decline. Cox proportional hazards models were used to examine the associations between PGS_GFR and each of incident coronary artery disease and incident myocardial infarction. Cause-specific hazard ratios for PGS_GFR were reported using the survival package,32R: A Language and Environment for Statistical Computing, Vienna, Austria.https://www.R-project.orgDate: 2020Google Scholar with the same covariates as the linear regression. Logistic regression was used to examine the relationship between baseline eGFR and presence of diabetes mellitus. Results from statistical models are adjusted unless otherwise specified. Area under the curve (AUC) was used to compare the results between models. Hosmer-Lemeshow test was used to assess calibration of model. Of the 19,114 total participants randomized to receive aspirin or placebo in the ASPREE trial, 12,141 had a baseline eGFR measure and provided a sample for genomic analysis. A further 328 were excluded as they did not have European genetic ancestry and 5 further individuals had stage 5 CKD. Thus, the final study population consisted of 11,813 genotyped European ancestry individuals. For the analysis assessing albuminuria (UACR), 2911 had a missing baseline UACR measure; hence, this analysis was confined to a subset of 8902 individuals. The baseline characteristics of the study sample are presented in Table 2. The mean age of the study population was 75.0 years (SD, 4.2 years), and 54.2% of participants were women. A total of 2904 participants (24%) met the CKDKDIGO definition, and 2071 participants met the CKDeGFR definition (eGFR < 60 ml/min per 1.73 m2). The median follow-time up during which participants took aspirin or placebo was 4.7 years. Following the cessation of the trial, additional follow-up continued (ASPREE-XT02 Longitudinal Data Set), with the median follow-up time increasing to 6.3 years.Table 2Baseline characteristics of the 11,831 ASPREE participants included in the analysesCharacteristicOverallN11,813Age, yr, mean (SD)75.0 (4.2)Age group, yr, n (%) 70–747215 (61.1) 75–792958 (25.0) 80–841269 (10.7) ≥85371 (3.1)Female gender, n (%)aGender is reported here according to that personally identified by each participant of their own gender.6401 (54.2)Smoker, n (%) Never6568 (55.6) Form